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Record W2968864677 · doi:10.1080/14649365.2019.1652929

Geographies of intransigence: freedom of speech and heteroactivist resistances in Canada, Great Britain and Australia

2019· article· en· W2968864677 on OpenAlexafffundabout
Catherine J. Nash, Andrew Gorman‐Murray, Kath Browne

Bibliographic record

VenueSocial & Cultural Geography · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContext (archaeology)SociologyPoliticsGender studiesAcademic freedomMedia studiesPolitical scienceLawHistoryHigher education

Abstract

fetched live from OpenAlex

Freedom of speech is a key way in which sexual and gender politics are contested. Heteroactivism names the ways that these discourses seek to open up space to push back against sexual and gender equalities,We focus on three different countries where distinctive framings about freedom of speech are deployed in diverse ways . Taking a transnational approach that explores interlinkages in discourses that touch down differently in each context, this paper looks at how freedom of speech claims are operative on university campuses in Canada, Australia and Great Britain. In Canada, Professor Jordan Peterson’s freedom of speech claims arguably enable transphobic, anti-feminist and anti-LGBT speech. In Australia, university academic Roz Ward’s ability to express controversial opinions was attacked because she runs an innovative Safe Schools programme seeking to protect LGBT students. In Great Britain, contesting ‘No Platforming’ through freedom of speech saw arguments that crossed left/right, progressive/conservative, eventually seeing Peter Tatchell defend discrimination against ideas, but not people. Heteroactivism offers an important frame to understand the pushbacks against sexual and gender rights which are integral to liberal democracies such as those in the UK, Canada and Australia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.293
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2019
Admission routes3
Has abstractyes

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